CBSE Class 10 Artificial Intelligence Syllabus, Units & Marks

Studying this subject? See the CBSE Class 10 Artificial Intelligence course, or read the units below first.

What is the CBSE Class 10 Artificial Intelligence syllabus?

CBSE Class 10 Artificial Intelligence is organised into 13 units. The full unit list, the topics inside each one and the exam pattern are below, taken from the syllabus we teach to.

CBSE Class 10 Artificial Intelligence unit list

What each unit covers

Part A: Communication Skills-II

• Methods and importance of communication
• Verbal, non-verbal and visual communication
• Communication cycle and feedback
• Barriers to effective communication
• Writing skills and basic English grammar

Part A: Self-Management Skills-II

• Stress management techniques
• Self-awareness and self-motivation
• Building self-confidence
• Goal setting and time management
• Working independently

Part A: ICT Skills-II

• Operating systems basics
• Basic file and folder management
• Computer care and maintenance
• Antivirus and data security
• Productivity tools overview

Part A: Entrepreneurial Skills-II

• Entrepreneurship and society
• Qualities and functions of an entrepreneur
• Types of business activities
• Myths about entrepreneurship
• Entrepreneurship as a career option

Part A: Green Skills-II

• Importance of green jobs
• Sustainable development
• Conservation of resources
• Reducing environmental impact
• Role of green economy

Part B Unit 1: Revisiting AI Project Cycle & Ethical Frameworks for AI

• Recap of AI domains and the AI Project Cycle
• Problem Scoping, Data Acquisition, Data Exploration, Modelling, Evaluation
• AI ethics: bias, data privacy and access
• Ethical frameworks and principles for AI
• AI and its societal challenges (e.g. self-driving cars)

Part B Unit 2: Advanced Concepts of Modelling in AI

• Rule-based vs learning-based AI approaches
• Supervised, unsupervised and reinforcement learning
• Classification and regression
• Decision trees
• Introduction to neural networks and deep learning

Part B Unit 3: Evaluating Models

• Need for model evaluation
• Confusion matrix
• Accuracy, Precision, Recall
• F1 Score
• Interpreting evaluation results

Part B Unit 4: Statistical Data / Data Sciences

• Introduction to Data Sciences
• Basic statistics: mean, median, mode
• Data collection, features and labels
• Data visualisation
• K-Nearest Neighbours (KNN) algorithm

Part B Unit 5: Computer Vision

• Concept of computer vision and applications
• Pixels, resolution and image features
• Image processing with OpenCV
• Convolution and feature extraction
• Convolutional Neural Networks (CNN)

Part B Unit 6: Natural Language Processing

• Introduction to NLP and applications
• Chatbots
• Text normalisation and tokenisation
• Bag of Words model
• TF-IDF and its applications

Part B Unit 7: Advance Python

• Python environment setup and Jupyter Notebooks
• Recap of Python basics and data types
• Lists, tuples and dictionaries
• NumPy arrays
• Working with Pandas and Matplotlib for data

Part C: Practical Work

• Practical file with a minimum of 15 Python programs
• Practical examination
• Viva voce
• Project work / field visit / portfolio
• Project-related viva voce

Exam pattern

Total 100 marks: Theory 50 + Practical 50. Theory written paper of 2 hours = Part A Employability Skills (10 marks) + Part B Subject-Specific Skills (40 marks). Practical (50 marks) = Practical file/programs (15) + Practical examination (15) + Viva voce (5) + Project work/portfolio (10) + Project viva voce (5).

Practical and project work

Hands-on practical component worth 50 marks. Students maintain a practical file with a minimum of 15 Python programs, sit a practical examination and viva voce, and complete a project (project work, field visit or portfolio) with a related viva. Practical work spans Python programming, data science, Computer Vision (OpenCV) and NLP tasks.

Reviewed by Kajal Ma'am (MCA), teaching CBSE Computer Science & Informatics Practices since 2006. Weightage per the official CBSE 2026-27 curriculum.